The bottleneck may be the solution, not the problem

As a highly consequential biological trait, a memory \u201cbottleneck\u201d cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication

Rationality as the Rule of Reason

The demands of rationality are linked both to our subjective normative perspective (given that rationality is a person-level concept) and to objective reasons or favoring relations (given that rationality is non-contingently authoritative for us). In this paper, I propose a new way of reconciling the tension between these two aspects: roughly, what rationality requires of us is having the attitudes that correspond to our take on reasons in the light of our evidence, but only if it is competent. I show how this view can account for structural rationality on the assumption that intentions and beliefs as such involve competent perceptions of downstream reasons, and explore various implications of the account

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Floquet engineering of the Lifshitz phase transition in the Hubbard model

Within the Floquet theory of periodically driven quantum systems, we demonstrate that an off-resonant high-frequency electromagnetic field can induce the Lifshitz phase transition in periodical structures described by the one-dimensional repulsive Hubbard model with the nearest and next-nearest neighbor hopping. The transition changes the topology of electron energy spectrum at the Fermi level, transforming it from the two Fermi-points to the four Fermi-points, what facilitates the emergence of the superconducting fluctuations in the structure. Possible manifestations of the effect and conditions of its experimental observability are discussed

Oligosaccharides from placenta: early diagnosis of feline mannosidosis

AbstractHigh-pressure liquid chromatography analysis of oligosaccharides from placentas allowed the diagnosis of α-mannosidosis in three litters of kittens. The chromatography also afforded a detailed comparison of the oligosaccharide pattern and levels in placenta, liver, brain, urine and ocular fluid of the affected animals. In all cases, two series of compounds were observed, with one or two residues of N-acetylglucosamine at the reducing terminus, respectively, and between two and nine mannose residues. This pattern is unlike that of human mannosidosis, and resembles that of ruminants, except that the major oligosaccharide contains three mannose residues instead of two

Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR)

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems

Convergent algorithms for protein structural alignment

<p>Abstract</p> <p>Background</p> <p>Many algorithms exist for protein structural alignment, based on internal protein coordinates or on explicit superposition of the structures. These methods are usually successful for detecting structural similarities. However, current practical methods are seldom supported by convergence theories. In particular, although the goal of each algorithm is to maximize some scoring function, there is no practical method that theoretically guarantees score maximization. A practical algorithm with solid convergence properties would be useful for the refinement of protein folding maps, and for the development of new scores designed to be correlated with functional similarity.</p> <p>Results</p> <p>In this work, the maximization of scoring functions in protein alignment is interpreted as a Low Order Value Optimization (LOVO) problem. The new interpretation provides a framework for the development of algorithms based on well established methods of continuous optimization. The resulting algorithms are convergent and <it>increase the scoring functions at every iteration</it>. The solutions obtained are critical points of the scoring functions. Two algorithms are introduced: One is based on the maximization of the scoring function with Dynamic Programming followed by the continuous maximization of <it>the same </it>score, with respect to the protein position, using a smooth Newtonian method. The second algorithm replaces the Dynamic Programming step by a fast procedure for computing the correspondence between C<it>α </it>atoms. The algorithms are shown to be very effective for the maximization of the STRUCTAL score.</p> <p>Conclusion</p> <p>The interpretation of protein alignment as a LOVO problem provides a new theoretical framework for the development of convergent protein alignment algorithms. These algorithms are shown to be very reliable for the maximization of the STRUCTAL score, and other distance-dependent scores may be optimized with same strategy. The improved score optimization provided by these algorithms provide means for the refinement of protein fold maps and also for the development of scores designed to match biological function. The LOVO strategy may be also used for more general structural superposition problems such as flexible or non-sequential alignments. The package is available on-line at http://www.ime.unicamp.br/~martinez/lovoalign.</p

Sexual Dysfunction in Jordanian Diabetic Women

OBJECTIVE—To estimate the prevalence of female sexual dysfunction (FSD) in diabetic and nondiabetic Jordanian women

Coupling effects in QD dimers at sub-nanometer interparticle distance

Currently, intensive research efforts focus on the fabrication of meso-structures of assembled colloidal quantum dots (QDs) with original optical and electronic properties. Such collective features originate from the QDs coupling, depending on the number of connected units and their distance. However, the development of general methodologies to assemble colloidal QD with precise stoichiometry and particle-particle spacing remains a key challenge. Here, we demonstrate that dimers of CdSe QDs, stable in solution, can be obtained by engineering QD surface chemistry, reducing the surface steric hindrance and favoring the link between two QDs. The connection is made by using alkyl dithiols as bifunctional linkers and different chain lengths are used to tune the interparticle distance from few nm down to 0.5 nm. The spectroscopic investigation highlights that coupling phenomena between the QDs in dimers are strongly dependent on the interparticle distance and QD size, ultimately affecting the exciton dissociation efficiency. [Figure not available: see fulltext.]